{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:03:13Z","timestamp":1793127793744,"version":"4.5.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BIDEP 2219 International Post-Doctoral Research","award":["1059B192101048"],"award-info":[{"award-number":["1059B192101048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized a dataset of CBCT volumes collected from 502 individual subjects. Initially, 250 CBCT volumes were used for automatic tooth segmentation and numbering. Subsequently, CBCT volumes from 251 patients diagnosed with periodontal disease were employed to train an AI system to identify various periodontal bone defects using a segmentation method in web-based labelling software. In the third stage, CBCT images from 251 periodontally healthy subjects were combined with images from 251 periodontally diseased subjects to develop an AI model capable of automatically classifying patients as either periodontally healthy or periodontally diseased. Statistical evaluation included receiver operating characteristic curve analysis and confusion matrix model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The area under the receiver operating characteristic curve (AUC) values for the models developed to segment teeth, total alveolar bone loss, supra-bony defects, infra-bony defects, perio-endo lesions, buccal defects, and furcation defects were 0.9594, 0.8499, 0.5052, 0.5613 (with cropping, AUC: 0.7488), 0.8893, 0.6780 (with cropping, AUC: 0.7592), and 0.6332 (with cropping, AUC: 0.8087), respectively. Additionally, the classification CNN model achieved an accuracy of 80% for healthy individuals and 76% for unhealthy individuals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study employed AI models on CBCT images to automatically detect tooth presence, numbering, and various periodontal bone defects, achieving high accuracy and demonstrating potential for enhancing dental diagnostics and patient care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/dmfr\/twaf011","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T15:52:26Z","timestamp":1738770746000},"page":"268-278","source":"Crossref","is-referenced-by-count":32,"title":["Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed 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